diff --git a/Analysis/CAP_ConsensusClustering.m b/Analysis/CAP_ConsensusClustering.m
index e017aa2..543d7a9 100755
--- a/Analysis/CAP_ConsensusClustering.m
+++ b/Analysis/CAP_ConsensusClustering.m
@@ -1,137 +1,137 @@
 %% This function performs consensus clustering over a range of K values
 % The goal is to provide a measure of how good each value of K is
 % 
 % Inputs:
 % - X is the data matrix (cell array with each cell n_DP x n_DIM)
 % - K_range is the range of K values to examine
 % - Subsample_type defines how subsampling is done: across items (data
 % points) if 'items', and across dimensions if 'dimensions'
 % - Subsample_fraction is the fraction of the original data points, or
 % dimensions, to keep for a given fold
 % - n_folds is the number of folds over which to run
 function [Consensus_ordered] = CAP_ConsensusClustering(X,K_range,Subsample_type,Subsample_fraction,n_folds,DistType)
 
     % Number of subjects
     n_subjects = length(X);
     
     n_items = 0;
     
     frames_index = cell(1,n_subjects);
     
     for s = 1:n_subjects
         frames_index{s} = n_items + (1:size(X{s},2));
         n_items = n_items + size(X{s},2);
     end
     
     % Number of dimensions
     n_dims = size(X{1},1);
     
     Consensus = zeros(n_items,n_items,length(K_range));
     Consensus_ordered = zeros(n_items,n_items,length(K_range));
     
     % Loop over all K values to assess
     for k = 1:length(K_range)
     
         disp(['Running consensus clustering for K = ',num2str(K_range(k)),'...']);
         
         % Connectivity matrix that will contain 0s or 1s depending on whether
         % elements are clustered together or not
         M = zeros(n_items,n_items,n_folds);
         I = zeros(n_items,n_items,n_folds);
         
         % Loops over all the folds to perform clustering for
         for h = 1:n_folds
             
             switch Subsample_type
                 case 'items'
                     
                     % Number of items to subsample
                     n_items_ss = floor(Subsample_fraction*n_items);
                     
                     % Does the subsampling
                     [X_ss,tmp_ss] = datasample((cell2mat(X))',n_items_ss,1,'Replace',false);
                     
                     % Vector
                     I_vec = zeros(n_items,1);
                     I_vec(tmp_ss) = 1;
                     
                     % Constructs the indicator matrix
                     for i = 1:length(I_vec)
                         for j = 1:length(I_vec)
                             if (I_vec(i) == I_vec(j)) && (I_vec(i) > 0)
                                 I(i,j,h) = 1;
                             end
                         end
                     end
                     
                 case 'dims'
                     
                     % Number of dimensions to subsample
                     n_dims_ss = floor(Subsample_fraction*n_dims);
                     
                     % Does the subsampling
                     [X_ss,tmp_ss] = datasample((cell2mat(X))',n_dims_ss,2,'Replace',false);
                     
                     % Constructs the indicator matrix
                     I(:,:,h) = ones(n_items,n_items);
                     
                 case 'subjects'
                     
                     % Number of subjects to use in the subsampling
                     n_subjects_ss = floor(Subsample_fraction*n_subjects);
                     
                     tmp = datasample(1:n_subjects,n_subjects_ss,'Replace',false);
                     
                     % n_frames x n_voxels
                     X_ss = (cell2mat(X(tmp)))';
                     tmp_ss = cell2mat(frames_index(tmp));
                     
                     % Vector
                     I_vec = zeros(n_items,1);
                     I_vec(tmp_ss) = 1;
                     
                     % Constructs the indicator matrix
                     for i = 1:length(I_vec)
                         for j = 1:length(I_vec)
                             if (I_vec(i) == I_vec(j)) && (I_vec(i) > 0)
                                 I(i,j,h) = 1;
                             end
                         end
                     end
                     
                 otherwise
                     errordlg('PROBLEM IN TYPE OF SUBSAMPLING');
             end
             
             % Does the clustering (for now, only with k-means), so that IDX
             % contains the indices for each datapoint
-            IDX = kmeans(X_ss,K_range(k),'Distance',DistType,'Replicates',1,'Start','uniform');
+            IDX = kmeans(X_ss,K_range(k),'Distance',DistType,'Replicates',10,'Start','uniform');
             
             % Builds the connectivity matrix
             M(:,:,h) = Build_Connectivity_Matrix(IDX,tmp_ss,Subsample_type,n_items); 
             
             clear I_vec
             clear X_ss
             clear tmp_ss
             clear IDX
         end
         
         % Constructs the consensus matrix for the considered K
         Consensus(:,:,k) = sum(M,3)./sum(I,3); 
         
         tree = linkage(squeeze(1-Consensus(:,:,k)),'average');
 
         % Leaf ordering to create a nicely looking matrix
         leafOrder = optimalleaforder(tree,squeeze(1-Consensus(:,:,k)));
         
         % Ordered consensus matrix
         Consensus_ordered(:,:,k) = Consensus(leafOrder,leafOrder,k);
         
         clear leafOrder
         clear Dist_vec
         clear test
         clear IDX
         clear M
         clear I
     end
 end
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